match climate statistics over decadal timescales. Simulating short-term regional climate change requires methods of downscaling large-scale information. Currently this is accomplished either through statistical approaches or by forcing a regional model with boundary conditions from a global climate model.


The concept of radiative forcing has provided a clear mechanism for conceptualizing the Earth’s climate as a closed system with a detectable metric of change: global mean surface temperature. The metric is easily understandable and readily correlated to global-scale geological, oceanographic, and biological changes (e.g., ice caps melting, sea level rising, ecosystems changing). The projected changes in climate have been translated into economic costs (with varying uncertainty), providing a direct relationship between radiative forcing and economic impacts. This relationship enables policy makers to consider the relative benefits of investments in new technologies, emissions regulations, carbon taxes, sequestration and offsetting, and emissions trading. This conceptual framework is illustrated in Figure 1-4.

For most policy applications, the relationship between radiative forcing and surface temperature is assumed to be linear, thereby making it possible to add different forcings to assess the overall climate impact. As discussed above, the linearity of response in several GCM experiments using the radiative forcing of homogeneously distributed greenhouse gases supports this approach as do summary diagrams compiled to compare different radiative forcings, such as those presented in the IPCC reports (see Figure 2-1 of this report). In these diagrams, it is often assumed that the bars from different sources may be added to give an overall effect although this is not entirely correct.

The simplification of complex, mechanistically disparate processes to the same radiative forcing metric, with the implication that positive forcings may cancel negative forcings, provides a way of easily communicating climate forcing factors and their relative importance to general audiences. However, a net zero global mean radiative forcing may be associated with large regional or nonradiative (e.g., precipitation) changes. Further, when forcings are added, uncertainties in individual forcings must be propagated, resulting in large uncertainties in the total forcing. Adding forcings also belies the complexity of the underlying chemistry, physics, and biology. It suggests that all effects on climate can be quantified by a similar metric without knowing, or needing to know, the details of the climate response as captured in feedback effects. Yet there are many aspects of climate change—including rainfall, biodiversity, and sea level—that are currently not related quantitatively, much less linearly, to radiative forcings.

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